Methods to detect anomalies and to measure water usage in pumping plants using energy consumption data

ABSTRACT

Water leaks and other anomalies in irrigation systems may be detected by analysis of energy consumption data captured from a utility power meter, and particularly energy data from smart meters that service water pumps. Furthermore, water usage can be measured indirectly from the energy required to move it given an understanding of its operating condition that ties water flow and electrical power. Unlike existing solutions that use water meters or other sensors, embodiments of the present method described herein detect water leaks and other anomalies from the electrical load for the water pump(s) and track the operating condition of the pump.

This application is a continuation of U.S. patent application Ser. No.14/506,567, filed Oct. 3, 2014, which claims the benefit of U.S.Provisional Patent Application Ser. No. 61/888,459 filed Oct. 8, 2013and is related to U.S. Utility patent application Ser. No. 14/506,484filed Oct. 3, 2014, all of which are incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates generally to methods of detectinganomalies in pumping plants that irrigate farmed lands. Irrigationmethods include surface irrigation techniques such as flood and furrow,and pressurized irrigation using a network of pipes such as sprinklers,center pivots, and drip nozzles.

BACKGROUND OF THE INVENTION

Pumps are a critical asset in agriculture. As water becomes scarcer dueto climate change, farmers have to dig deeper to have access toground-water and to compensate for lack of rain or State water through anetwork of canals. The depth of a well has increased from a few hundredfeet to a few thousand feet. The cost per feet increases as well astraditional well drilling techniques are not sufficient. Oil and gasdrilling techniques are now being used to go down to the appropriatedepth. As a result, water pumping is becoming very energy intense. Thedeeper the water is, the more energy it takes to bring it up and toirrigate fields. In 2014, California spent an additional $454 in energyto extract ground water to compensate for the drought.

The pumping plant can be damaged by natural wear-and-tear of thepropeller, a falling water table, an electrical failure, a malfunctionin the filter that removes sand or other debris, and any external thatchanges the pressure of the overall pumping plant. In the case ofpressurized irrigation systems, leaks will change the pressure and thenormal operating condition of the pump. Leaks can be caused by a worker,an animal or a machine. Leaks can also be caused by wear and tear. Leaksare very labor intensive to locate (ranchers have to walk every otherrow in a large field). They usually go undetected until there isphysical damage to a crop or to property, or when an increase in utilitybills is observed by the farmer, which may be months after the leakstarted.

Water pumps are used to distribute the water throughout an irrigationsystem. Anomalous behaviors makes it more difficult to automate theirrigation system by a control system; it also makes the performance ofthe water pumps less predictable and prevents the effective use ofenergy management systems. Otherwise energy management systems could be:(1) used to leverage flexibility in the pumps that can be sold to autility market; or (2) integrated with energy storage systems to use arenewable energy source onsite rather than conventional sources from thegrid. See, for example, European patent application publication numberEP20120181445 “Methods and apparatus for controlling irrigationsystems”, and U.S. patent application Ser. No. 13/844,605 “Digitalelectrical routing control system for use with electrical storagesystems and conventional and alternative energy sources.”

Currently, anomalies are detected by performing a pump efficiency testthat measure the water flow, the internal pressure of the pump, and thepower drawn by the pump. This defines the operating condition of a pumpthat can only be of certain values for each type of pump. Pumpmanufacturers provide pump curves that describe how the pump operates inwater flow gallons-per-minute and pressure in feet-of-head for varioussizes of pipes. It also provide the overall pump efficiency inpercentage and the power drawn in horse-power. Pump tests are laborintensive and are recommended every few years to maintain the pump andreduce the load on power utility grids.

Leaks are a particularly of interest because they waste both water andenergy. For water systems beyond the utility company's water meter, suchas in farming operations, leaks have been typically identified by theappearance of wet areas on the property, more vigorous vegetation, or byseeing significantly larger utility bills weeks after the leak firstbegins.

Water is becoming more valuable as it becomes scarcer. Therefore, morefarmers are now measuring how much water they consume. They can alsooptimize crop yield by controlling the amount of water applied to thefield at particular time of the year. Current methods of measuring waterusage include welding a flow meter into a piping system and takingregular manual readings, or by estimating it from the number of hoursthe pump was on. Both methods are imprecise and are labor intensive. Forinstance the operating condition of the pump may change over time, orthe flow meter that was installed is not properly installed. Farmers arenot always inclined to find a remedy to have more precise measurements,or maintain water records at all as they might be under the scrutiny ofregulating bodies to use too much water. More than seventy percent ofthe world's fresh water supply is used for agriculture.

There is a need for improved methods of detecting anomalies in pumpingplants and measure water usage for irrigation.

SUMMARY OF THE INVENTION

Water leaks and other anomalies in irrigation systems may be detected byanalysis of energy consumption data captured from a utility power meter,and particularly energy data from smart meters that service water pumps.Furthermore, water usage can be measured indirectly from the energyrequired to move it given an understanding of its operating conditionthat ties water flow and electrical power. Unlike existing solutionsthat use water meters or other sensors, embodiments of the presentmethod described herein detect water leaks and other anomalies from theelectrical load for the water pump(s) and track the operating conditionof the pump. These methods have the advantage of not requiring any extrahardware at the site of the irrigation system. In embodiments, methodsof the present invention are very scalable due to the energy usage datacaptured from smart meters at the site of the irrigation system and madeavailable through interfaces such as the Energy Service ProviderInterface (ESPI) from Green Button that makes energy data available tomillions of users and third party applications. See Energy ServiceProvider Interface at http://openespi.org/. Analysis of the energy datais done using an algorithm based on statistical analysis. Furthermore,machine learning programs can further identify the type of anomaly byrecognition of electrical signatures of a water pump.

Further embodiments of the present invention include systems for theimplementation of anomaly and/or leak detection and notification methodsaccording to the aforementioned processes.

Further embodiments of the present invention include methods to maintainautomated water records at the site of the pump.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects and features of the present invention willbecome apparent to those ordinarily skilled in the art upon review ofthe following description of specific embodiments of the invention inconjunction with the accompanying figures, wherein:

FIG. 1 shows a flow chart of a leak detection and notification method,according to some embodiments of the present invention;

FIG. 2 shows a schematic of a system and method of leak detection andnotification according to a first embodiment of the present invention;

FIG. 3 shows an example of energy data collected for an irrigation pump(energy in kWh as a function of time in days), along with an errorsignal generated by a PAM algorithm, according to some embodiments ofthe present invention;

FIG. 4 shows a further example of energy data collected for theirrigation pump of FIG. 3, where the calculated error signal exceeds analarm threshold generating an alarm, according to some embodiments ofthe present invention;

FIG. 5 shows an extension of the energy data of FIG. 4 by a few days,where the calculated error signal exceeds a warning threshold generatinga warning, according to some embodiments of the present invention;

FIG. 6 shows an example of a unique electrical signature associated witha leak in a particular part of the irrigation system, according to someembodiments of the present invention;

FIGS. 7A-E shows a schematic of a system and method of electricalsignature collection to allow for identification of leaks in differentparcels of an irrigated property, according to some embodiments of thepresent invention and energy signatures associated therewith;

FIG. 8 shows a schematic of a system and method of leak detection andnotification with multiple electrical devices attached to the powermeter, according to some embodiments of the present invention;

FIG. 9 shows a schematic of a system and method of leak detection andnotification with a finer-grained data collection capability andfiltering, according to some embodiments of the present invention;

FIG. 10 shows a schematic of a system and method of leak detection andnotification for a water distribution system for homes, according tosome embodiments of the present invention;

FIG. 11 shows an example of a normal daily electrical load for a waterpump, the data being collected according to some embodiments of thepresent invention; and

FIG. 12 shows an example of an erratic electrical load for a water pumpdue to an irrigation leak, the data being collected according to someembodiments of the present invention.

FIG. 13 illustrates an electrical load of a winery with a pump and otherappliances including an HVAC (a). One leak occurred in February 2014 andthe winemaker at the site of the leak (b) uses now our alert solution toavoid future leaks.

FIG. 14 illustrates a curve describing the operation conditions of apump model (e.g., 7GS20) with a capacity, Q. in m3 per hour and a TotalDynamic Head (TDH) in feet.

FIG. 15 shows an example of distinguishing between multiple operatingconditions.

FIG. 16 shows a method to measure water usage in a pumping plant fromenergy data. It leverages anomaly detection to track the accuracy of themeasurement and alert the farm in case it needs to be repaired or testedfor efficiency.

FIG. 17 is a picture of flow meter with both gallon-per-minute readingand cumulative acre-feet measurements. Grower does not keep waterrecords from the flow meter as they use know-how developed over a longperiod of time to decide how long to run the pump for across the season.Water application late can be used to control the time of harvest forinstance.

FIG. 18 shows a broken flow meter due to a leak in the piping system.

FIG. 19 illustrates energy profiles (b) of a pumping plant with a wellpump, a filter and a booster (b).

FIG. 20 shows the energy profile of April 8 (b) has one anomalycorresponding to a change in the position of the valve to mitigate lackof pressure due falling water table. In contrast, a leak that occurredearlier that day (a) is not visible.

FIG. 21 illustrates a method to detect an anomaly in a pumping plant forirrigation

FIG. 22 shows implementation of the data processing step into two steps:one to detect when an anomaly occurs (algorithm 1) and one step toidentify what the anomaly is (algorithm 2).

FIG. 23 shows a pump curve with two operating conditions beforeperturbation (green) and after perturbation (red).

FIG. 24 shows an implementation of the anomaly detection, the leakidentification, and the water calculation part of a web applicationcalled Pump Monitor.

FIG. 25 describes the 5 main steps in the Pump Monitor program.

FIG. 26 illustrates the anomaly detection using statistical dataanalysis using mean and standard deviation as features.

FIG. 27 shows water flow estimation from power measurement. If the pumpdoes not work at its normal operating condition (P0), the water flown iscalculated from a polynomial decomposition for a small perturbationDeltaP.

FIG. 28 shows water measurements from energy data across one completeseason against Evaporation-Transpiration (ET) values to grow a crop. Thecrop is almond. Reduced Deficit Irrigation from ET values are alsoplotted. Growers can save water by comparing the quantity of appliedwater against RDI targets without risking losing the crop.

FIG. 29 shows examples of signatures in time-domain (left column) andfrequency domain (right column).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described in detail with reference tothe drawings, which are provided as illustrative examples of theinvention so as to enable those skilled in the art to practice theinvention. Notably, the figures and examples below are not meant tolimit the scope of the present invention to a single embodiment, butother embodiments are possible by way of interchange of some or all ofthe described or illustrated elements. Moreover, where certain elementsof the present invention can be partially or fully implemented usingknown components, only those portions of such known components that arenecessary for an understanding of the present invention will bedescribed, and detailed descriptions of other portions of such knowncomponents will be omitted so as not to obscure the invention. In thepresent specification, an embodiment showing a singular component shouldnot be considered limiting; rather, the invention is intended toencompass other embodiments including a plurality of the same component,and vice-versa, unless explicitly stated otherwise herein. Furthermore,the present invention encompasses present and future known equivalentsto the known components referred to herein by way of illustration.

The general method to detect and notify a water leak is described in theflow diagram of FIG. 1. It consists of an energy data capturing step(“energy data storage”), a data processing step (“data processing”), andan alarm signal step where the alarm signal is triggered by a threshold(“water leak alarm”).

In a first embodiment (FIG. 2), the energy data is captured from a smartmeter operated by a utility (e.g., PG&E). Utilities use advancedmetering infrastructure to automatically collect energy data for billingpurposes. The data can be made available to third parties thanks to theGreen Button interface. The energy data captured at regular intervals(e.g., 15 minutes) are made available typically once a day. A server cancollect the data using a communication network such as the Internet. Theenergy data is stored in a secure database. In one embodiment, thedatabase is secure to protect the privacy of the user. For example, theuser can be a rancher such as a horse breeder or a farmer such as avine-grower.

The data is processed using an algorithm. In one implementation, thealgorithm is based on statistical data analysis. For the purpose oftesting, a Partitioning Around Medoids (PAM) of class 3 algorithm wasused. FIG. 3 shows an error signal (lower sigmoid) generated by PAMclassification with 3 medoids; the raw energy data is in 15 minuteincrements over 13 months, with the error signal (lower sigmoid) showingthat signature for a leak is to not go back to zero energy consumptionwhen the irrigation cycle is off.

Historical data (e.g., 13 months) can be used to generate one or morethresholds. In one embodiment, only one threshold is used to send analarm signal to the user. In another embodiment a second lower thresholdis used to send a warning message. For the purpose of testing, anemergency alarm is sent via a text message to reach the user on his orher portable phone. In another embodiment, a warning message is sent byemail.

The method described above was tested in summer 2013 at a ranch inCalifornia. An apparatus was built as described above. A leak wasreported in September (FIG. 4)—the error signal went over the alertthreshold indicating a leak. The customer validated that the methoddetected a leak the user did not know about. The user was able to fixthe leak, which resulted in thousands of gallons saved and hundreds ofkilowatt-hours saved. More importantly, detection of the leak preventedphysical damage to the property. A previous leak had flooded a fieldover time and ruined the property landscape. In some situations a leakcan cause damage to crops such as, but not limited to, vines, orchards,and lettuce.

It has been found that the error signal tends to creep up (slowlyincrease) before a significant leak occurs. A warning threshold can beset at a fraction (a third, or other fraction, for example) of the alertthreshold to bring attention to a developing leak. In one instance, aleak was predicted three days before it actually happened using thistechnique. For example, later in September, the statistical indicator(lower sigmoid) increased again and reached the warning threshold (FIG.5). A warning email was sent predicting a leak. A few days later, thestatistical indicator reached the alert threshold (FIG. 5) indicating aleak. An alarm text message was sent to the user of the irrigationsystem.

The method described above can be enhanced to not only detect when aleak occurs but also where it is located. This can save many man-hourslooking for the leak on a large field. Various leaks create differentsignatures in the electrical load (FIG. 6 shows electrical signaturesbefore a catastrophic leak). The signatures can be recognized by machinelearning algorithms provided with data sets that assign signatures tovarious parcels on the property. The assignment of signatures todifferent parcels on a property can be done in a training process suchas shown in FIG. 7A-7E, with FIG. 7B-7E illustrating how the differentsignatures, as described above, are used to determine location. FIG. 7Ashows the overview of the system. FIG. 7B illustrates the energy loadprofile for a pump connected to two parcels: one parcel is irrigated inthe morning, one in the evening. In the load profile shown in FIG. 7C,there is a leak on the first parcel. In the load profile shown in FIG.7D, there is a leak on the second parcel. In the load profile shown inFIG. 7E, a leak on the second parcel was fixed but there is still one onthe first parcel.

In another embodiment, a gateway is collocated with the meter, as shownin FIG. 8, to capture finer-grained data at shorter intervals (e.g., 1minute). The Device 2, Device n, etc. in FIG. 8 are other electricaldevices such as another water pump, an HVAC system, a refrigerator, alighting system, or any other appliance, which complicate the energydata collected, such that filtering is required. The data is captured bya server using a communication network. The finer-grained data can beused to detect the leak in a real-time rather than within a 24-hourperiod. The finer-grained data provides more detailed electricalsignatures that can be used to identify the location of the leak moreaccurately.

In yet another embodiment, more than one electrical device is attachedto the meter (FIG. 9). The energy data related to the water pump isextracted using a filter. The method described above is used to processthe extracted data and to generate an alarm when a leak is detected(FIG. 9).

The method herein can be generalized to a water distribution systemwhere a large pump distributes water to various homes, as shown in FIG.10. It has not been demonstrated, but a leak at a home will generatechanges in the pumping system. However, it remains to be tested that theresponse in electrical load is large enough to be detected reliably by adata mining algorithm.

Finally, the method described herein can be used to remove anomalies inthe way water pumps are run. This is particularly useful to automateirrigation from historical patterns. If not caught early using themethod described above, leaks can create significant changes in theelectrical load over time, which prevents predictable and repeatableautomation. A normal load was captured for one test site (FIG. 11). Anabnormal load was captured during a long-term leak (FIG. 12).

As described above, one aspect of the invention is the detection of awater leak in a system at a winery where several appliances includingthe pump are connected to the meter, and energy data can be used toestimate the amount of water leaked. One year of energy load wascaptured including the period when the leak occurred in February (FIG.13-a). The winemaker stands next to the leak (FIG. 13-b). The pumpoperating at the winery has one speed that corresponds to the normaloperating condition on the pump curve for that particular pump model andfor the size of the pipe connected to the pump (FIG. 14).

Further, specific embodiments described herein recognize that one candistinguish the case of traditional one-speed pumps (with one operatingcondition) and more recent variable-speed pumps (with multiple operatingconditions) that are driven by a Variable Frequency Drive (VFD) controlsystem. An example is given in FIG. 15.

Furthermore, described herein is a method to measure water using energydata that uses anomaly detection to verify that the measurement iscorrectly calibrated. It has been known that the relationship betweenwater flow and power in a pumping system varies over time. As a result,power utilities recommend farmers to test the overall pumping plantefficiency (OPE) regularly to save on energy. That factor, however,cannot be used to measure water consumption accurately withoutmonitoring the health of the pumping plant. Therefore, a method tomeasure water usage in pumping systems from energy consumption data isdescribed in FIG. 16, which has important application to measureground-water extraction, instead of installing a flow meter asconventionally done which requires a welding job and manual readings. Aflow meter is shown in FIG. 17. A flow meter that is not properlyinstalled is shown in FIG. 18.

Furthermore, in another embodiment water lost during a leak is measuredfrom the energy wasted during a leak. We tested the embodiment at thewinery where a leak occurred. We compared the results from the methoddescribed herein with results from a traditional flow meter. Theincreased energy consumption due to the leak can be seen in the energytable of FIG. 13-a, which is for a water leak that lasted 41 days. Theleak did not change the operating condition of the pump (higher power)but it changed the frequencies of the pumping cycles to compensate forthe loss of water in the reservoir tank. Three different methods tocalculate the energy wasted to move the water during the leak aredescribed. The first method (method a) is simply compare the energy loadof one month with the energy load of a previous month, which leads to anapproximation unless the leak match the billing period, and allows tosee what the leak cost him in additional electricity. A second method(method b) is to compare the energy consumed during the leak of 41 dayswith the energy consumed during the same period a year earlier. A thirdmethod (method c) is to sum the error signal from the PAM algorithm usedto detect the leak, which is proportional to the energy wasted by theleak. The results of energy savings derived are listed in Table 1.

TABLE 1 Energy wasted during the leak that lasted 41 days from Jan. 22to Mar. 3. Estimation of energy wasted (kWh) Event Start date DurationMethod a Method b Method c Leak Jan. 22, 2014 41 days 1,489 1,524 1,258The potential savings at the winery are significant. The 12-month energyconsumption is 12.4 MWh, and the leak represents more than 10% of theannual load. The measurement methods are consistent with each other butmethod (c) was off by 14% from the median of the three values.Since there is a direct relationship between the water flowing in apumping system and the power needed to move it, one can also measure theenergy and water lost as a result of a leak. The State of California,for example, requires that pumps are regularly tested for overallpumping plant efficiency (OPE). A properly designed and maintainedpumping system has an efficiency higher than 50%. OPE depends on threeparameters: the capacity (gallons per minute), the input power (horsepower), and the pressure called total dynamic head (feet per head). Theequation for OPE is provided in Equation 1.

${O\; P\; E} = \frac{Q \times {TDH}}{3960 \times {HPinput}}$Equation 1. The Overall Pumping plant Efficiency (OPE) can be calculatedfrom water flow Q (gallons-per-minute), the pressure TDH (feet of head)and the input power HPinput (horse-power).Every pump has a set of possible operating conditions described in a setof curves provided by the pump manufacturer. FIG. 14 describes the setof curves for a well pump (Gould 7GS-20), which was intended to functionby pumping between 7 and 9 gallons-per-minute (gpm). Knowing theelectrical power of the pump HPinput from the energy data (FIG. 13-a).HPinput is 3.1 kW or 4 hp. The water flow Q from the pump cycles in theenergy data (FIG. 13-a) and the water data (Table 1) can be estimatedduring a fixed period, for instance February 3 to March 3. The result isa flow, Q, of 8.2 gallons-per-minute, which is consistent with theoperating range of the pump. From the pumping curve (FIG. 14), thepressure TDH is 630 feet. With all three parameters known, the OPE canbe calculated. The result is 32%, which is low, and would allow an alertof the problem to be sent.Knowing the OPE and the TDH of the pumping system also allows one toestimate the water used during a period of time using a variation ofEquation 1 that links water and energy rather than water flow and power.It is provided in Equation 2 for water in gallons and energy in kWh.

$\frac{Water}{Energy} = {177,250\frac{O\; P\; E}{TDH}}$Equation 2. Relationship between energy (kWh) and water (gallons) in apumping plant with known OPE (%) and TDH (feet) parameters.Using the example lead above, the amount of water wasted during the leakfrom January 21 to March 3 is calculated with the results in table 2.The estimates range from 113,000 to 131,000 gallons of water, which iscomparable with the estimate of 100,000 gallons from the water meterbetween February 3 and March 3. The smart power meter actually providedmore accurate water records because it took regular one-hourmeasurements. The leak lasted longer than the winemaker thought becausehis crew took less frequent measurements with the water meters.

TABLE 2 Estimation of water wasted during the leak of 41 days based onenergy measurements. Estimation of water leaked (gallons) Event Start atDuration Method a Method b Method c Leak Jan. 22, 2014 41 days 131,356137,203 113,260

Furthermore, in yet another embodiment from energy consumption data, apolynomial decomposition of the water flow according to changes in thepower of the pump can be used to estimate more accurately the amount ofwater used than using an average operating condition. The operatingcondition can vary due to anomalies, or in the case of VFD pumps due tochanges in the load in the irrigation distribution system.

Furthermore, in yet another embodiment of the method to measure waterfrom energy consumption data, a machine learning algorithm calculatesthe amount of water applied. Regression algorithms such gradient descentcan train Support Vector Machines from known data sets that consist ofnormalized irrigation cycles in an input vector X and of watermeasurements taken with traditional methods. A vector of weightedcoefficients W will be created among thousands of training examples, andit can be applied to measure water from a pump energy data. Thedisadvantage of this approach is to acquire training data sets. Theadvantage is cover linear and non-linear situations where the operatingcondition of the pump changes significantly beyond a small perturbation.

Pumps that are turned on and off manually rather than automaticallypresent a further embodiment that will now be described with respect toFIG. 18. The pumping plant illustrated consists of a well pump (40 hp),a gravity-fed filter, and a booster pump (25 hp). The energy load of thecombined pumps is shown in FIG. 18. The irrigator turns the pumpingplant on and off manually. As a result, if there is a leak it will notcause an increase in the base load of energy. This situation isdifferent from situations in which pumps are controlled automatically tomaintain a minimum pressure. If there is a leak, the pump will not stopand continue to draw energy.

Further described here is how another anomaly can make it even moredifficult to detect water leaks during an irrigation cycle. Energyconsumption data though still provide useful information. An example ofleak (FIG. 19-a) was not detected during an irrigation cycle because itdid not cause a change in the energy load. In contrast, the fallingwater table of the well caused the booster pump to cycle and the energyload decreased overtime (FIG. 19-b).

In a further aspect, recognition that other anomalies in water pumpingsystems exist and can be recognized, in addition or instead of, theanomaly associated specifically with a water leak. Descriptions of thataspect, as well as others, are provided in the examples and discussionthat follow.

As such, in a specific aspect described herein is a method to includeanomalies other than water leak alerts and send an alert with a textmessage that identifies the other anomaly (FIG. 20). As is described, afirst program (also referred to as first algorithm) which runs softwareon the computer system is used to detect that there is an anomalousbehavior (FIG. 21). A second program (also referred to as secondalgorithm) which runs software on the computer system is used toclassify the previously detected anomaly among known categories (FIG.22). The list of categories can include leak, falling water table,electrical failure, and others. Described is thus a matrix of“electrical signatures” in time-domain and frequency-domain for thosecommon anomalies (FIG. 29). They can form a set of classificationalgorithms written in software and executed by the computer system.

Furthermore, classification algorithms include supervised learningtechniques such as Support Vector Machines or Neural Networks, andunsupervised learning techniques such as Partinionin Around Medoid andK-Mean.

Furthermore, we recognize that in order to detect an anomaly in apumping plant using energy data, its energy signature must be greaterthan the noise. The variation of the energy consumption can be estimatedin several ways; one way is listed in Equation 3.

$\mspace{76mu}{{\Delta\; E} = \frac{{E\;\max} - {E\;\min}}{E\;{mean}}}$Equation 3. Estimation of noise in a pump by diving the maximumvariation divided by the average in an on-state.For relatively good pumps, it is usually lower than 5% of the cycle'smean of energy consumption. Filtering can help for known sources ofvariation (e.g., other appliance connected to the same meter). Althoughit is hard to estimate how the pressure will change due a specificanomaly, using pump curves that are then stored in electronic form andaccessed by the software that is created based on the principlesdescribed herein, one can determine the minimum change required tocreate a variation that is greater than the noise ΔE. A linearregression is useful. For example, given the pump curve in FIG. 23, theoperating point is highlighted in green: Water Flow=580 gpm and TDH=112feet of head. A change of 5% of the energy consumption would require,according to the pump curve, the operating point to move on the curve toa new point such as the one highlighted in red. The change represents amodification of 54 gpm in water flow. The example is a first orderapproximation. An anomaly such as a leak that affects the pressure (TDH)will also affect both the water flow and the pump efficiency (OPE).Thus, the relationship between energy consumption and pressure dependson the pump curve, and so varies over every pump model.

In one embodiment of the general methods described above to detectanomalies and measure, the following Pump Monitor program wasimplemented. It provides intelligent answers for growers based on theirsmart meter electricity usage (FIG. 23). The general process of theprogram is described in FIG. 24.

A. Account Setup

The account setup collects enough data for each pump to be monitored.

The data required for electrical anomaly (including leak) detection isthis:

-   -   1. Pump location (geo-coordinates)    -   2. Electrical meter information including utility login    -   3. Matching of pumps to the meter    -   4. Pump type (irrigation, VFD/Pressurized system)    -   5. Other equipment at this meter (e.g. barn, winery, house)        Additional data is required to estimate water usage. Most of        these will come from a pump test, or can be found by inspection        of the equipment.    -   1. Gallons per minute rating (or equivalent flow rating)    -   2. Water source (well or canal/stream)    -   3. Total Dynamic Head of the pump system    -   4. Operational pump efficiency    -   5. Rated pump horsepower(s)        Additional data is desirable to increase accuracy of water        usage. This includes information describing the pump and well.        Examples of this information are:    -   1. Pump model(s) & type & size    -   2. Impeller size(s)    -   3. The pump data curves from manufacturer for all pumps within        the system.        B. Benchmark of Historical Data        The benchmark process reads the previous 13 months of electrical        history and results in three outputs. The first two are intended        for use by the account representative and customer to ensure the        pump is functioning properly so that electrical monitoring and        water estimation can be performed.    -   1) A report showing historical summary of usage        -   a. Average irrigation hours per day/week across the season        -   b. Power usage by month        -   c. Estimated water usage (if GPM and other parameters are            available)    -   2) Analysis of pump operation        -   a. Any anomalies noticed        -   b. Comparison of stated electrical horsepower and actual            horsepower. This determines if the pump is operating near            the design specification    -   3) Internal calibration of the algorithm for this specific pump        C. Periodic Analysis        The daily or hourly analysis looks at the electrical usage from        the new and previous internals and looks for problems and also        estimates water used. There are several different algorithms        used, depending on the pump type (large well pump, VFD,        pressurized, canal pump). Each of these algorithms has two        parts: detect a problem, then classify what kind of problem was        found. The calibration done during the benchmark determines the        “normal” parameters. There are two primary results:    -   Text alerts in the event anomalies are found. Alerts may be of a        general anomaly, or they may have more specific information        based on the classification of known anomaly types.    -   A daily report available on the website about energy usage and        estimated water        Texts that are received in reply are logged and used to track        response to the alert.        D. Monthly Report        The monthly report is sent by email to customers that have        requested it. This report summarizes the electricity and water        used in the previous month, along with any reported problems and        how quickly they were fixed.        E. Yearly Report and Recalibration        The purposes of the yearly report:    -   Provide report for account managers to help customers plan for        the next season    -   Adjust calibration parameters based on the full season of data        to improve accuracy for the subsequent season        All of the steps in the benchmarking are performed, with focus        on the season (January 1-December 31) or other growing season as        determined by the crop.        There are additional elements:    -   Comparison of year to year seasonal effects, incorporating        reference evapotranspiration parameters and other weather data        (e.g. California Irrigation Management Information System)    -   The calibration of algorithms may include multiple seasons of        data to allow better season-specific algorithms        The Pump Monitor program is self-calibrating based on the        historical data. We now explain in more details the periodic        analysis that consists of the anomaly detection, the anomaly        classification, and the water calculations. For illustration, we        only show mean and standard deviation of the energy load (FIG.        25). More features can be used to improve accuracy of the        algorithm. The algorithm has 5 steps:        1. Cycle extraction. This takes the energy data for the current        time period and appropriate previous days to determine the start        and stop time of each irrigation cycle. Two different approaches        are used to ensure accuracy. The first approach uses a power        threshold comparison based on the data from calibration, along        with cycle length. It works extremely well on “square wave like”        power signals. The second approach uses a power density        clustering approach to determine when cycles begin and end. This        approach works better for pumps where the meter has additional        loads or permanent baseline changes have occurred.        2. Feature extraction. Given the cycle start & stop, the next        step is to extract features from the consumption points        contained in the cycle for comparison. There are several        features that can be used. For illustration, Drawing 1 above        shows how the mean μ and standard deviation σ are used.        3. Anomaly detection. The mean for this cycle is compared to the        nominal mean as determined by benchmarking, as well as a        threshold selected during calibration. The standard deviation of        the cycle is also compared to thresholds. If either value is out        of the expected range, an anomaly has been detected.        4. Anomaly classification. The pump monitor uses a set of        classification algorithms. Some of them depend on the cycle        extract and scoring described above, others combine anomaly        detection and classification in a single processing pass for        efficiency. Once an anomaly has been found, the data in that        cycle is further examined to compare to a set of known features        for classification. Examples include water draw-down due to        falling water table, and suspicious electrical activity. These        pattern recognition approaches are based on standard        methodologies.        5. Leak detection. For automatically controlled well pumps (such        as a pressurized system shared with a house or other facility),        there is a unique power signature that represents a leak. We can        use the existing classifier based on PAM.        6. Water calculation. The pump monitor computes water usage        based on energy used and knowledge of the pump. A way to        calculate water usage to look at the “on” time of the pump and        look at the rated flow in GPM either from a recent pump test, or        from original design parameters. This does not reflect the        reality, since often pumps are operated outside of design        parameters, and the last pump test may have been done years ago.        As such, described herein is a better way to compute water usage        based on the power used. The actual water flow and power used        are based on several factors, including Total Dynamic Head        (TDH), the Operational Pump Efficiency (OPE). The equation is

${{Power}({KW})} = {0.746*\frac{{GPM}*{TDH}}{3960*O\; P\; E}}$The equation is misleading though: the GPM, TDH and OPE are notindependent once the pump is installed. A specific pump has a curve(provided by the pump manufacturer) that relates the 4 variables above.An example is provided in FIG. 26.Diagram 2—pump curve relates power, GPM, TDH and OPE

The results of the last pump test (or design parameters) establish anominal operating condition relating power used and GPM:GPM_(nom) =K ₁ *P ₀To compute a water usage from power, we use the equation above as wellas the pump curve to create a calibration that allow estimation of waterflow based on the deviation from nominal power input.GPM_(actual) =K ₁ *P ₀ +K ₂ *ΔP+K ₃ *ΔP ²In the above, the constants K come from fitting a polynomial to the pumpcurves, based on the last pump test that determines the location on thecurve of the nominal location. The variable ΔP is computed for eachinterval based on the power usage deviation from the nominal conditionsof the last pump test.

In another embodiment, we implemented the water measurement method tocompare with the expected amount of water that should be applied to growthe crop. If too much water is applied, an alert can be sent to thegrower who can turn the pump off. If not enough water is applied, andthe plant is stressed as a result, an alert can also be sent to so thegrower turns the pump off. Plants use water through transpiration, andirrigated water is also lost due to evaporation. A popular industrypractice is to schedule based on the evaporation and transpiration ratesof the crop, also known as evapotranspiration (ET). ET values areavailable as a public service in many states. For instance, CIMISprovides daily ET values in California. Growers can register online. Inone example the volume of water from the CIMIS model for the orchard wascompared with the actual amount of water applied to the field. The waterapplied to the field was calculated from energy data. The pump used forirrigation of the field did not have a flow meter. FIG. 27 summarizesthe ET values over one season at that location (line). The amount ofwater actually applied shown as well (columns). It is apparent that thefield is irrigated much less than the ET model requires during summer.This is because almond is more water resistant during the months of Julyand August when there is hull split and the crop is getting ready toharvest. The approach to reduce irrigation at non-critical times isreferred to as a Reduced Deficit Irrigation (RDI) model. Researchuniversities such as UC Davis and CSU Fresno have studied this indetails for a number of crops including almond to save water.

In yet another embodiment, RDI schedules can be compared to the waterapplied so the grower can save water and energy without taking the riskof losing crop. A daily text service is particularly appropriate asextended periods of stress can have a dramatic impact on the crop atparticular times of the year. FIG. 27 also summarizes RDI values (dottedline) for the previous example at an orchard. In general the irrigationof the field follows the RDI model except for two events:

-   -   In January, the grower irrigated the field much more, which was        expected the field was not irrigated during winter. The soil has        a certain water holding capacity and acts as a sponge or small        reservoir. The soil needed to be replenished. In January, the        field was irrigated to maintain an appropriate level of moisture        in the soil. The grower would have received a text alert in        December unless this particular seasonal effect was taken into        account in the program.    -   In August, the field was irrigated much less, which was due to        repairs to the drip irrigation lines that month which led to no        irrigating as a result. As a result the grower would have        received an alert to turn the pump on.

Although the present invention has been particularly described withreference to the preferred embodiments thereof, it should be readilyapparent to those of ordinary skill in the art that changes andmodifications in the form and details may be made without departing fromthe spirit and scope of the invention.

What is claimed is:
 1. A method of providing an automated water recordfor an irrigation system using ground water extraction and that includesan electrical water pump that operates using electrical energy and whichis connected to an energy meter that periodically provides for aprevious period of time energy usage data for the previous period oftime, the method comprising the steps of: receiving and storing theenergy usage data associated with the irrigation system for the previousperiod of time in a database; using the energy usage data for theprevious period of time to estimate water usage for the previous periodof time using a data processing computer system coupled to the databasethat operates upon the energy usage data, wherein the data processingsystem that operates upon the energy usage data uses an averageoperating condition during a time period in obtaining the water usageestimate during the same time period; and preparing the water recordbased upon the estimated water usage using the data processing computersystem.
 2. A method of providing an automated water record for anirrigation system using ground water extraction and that includes anelectrical water pump that operates using electrical energy and which isconnected to an energy meter that periodically provides for a previousperiod of time energy usage data for the previous period of time, themethod comprising the steps of: receiving and storing the energy usagedata associated with the irrigation system for the previous period oftime in a database; using the energy usage data for the previous periodof time to estimate water usage for the previous period of time using adata processing computer system coupled to the database that operatesupon the energy usage data, wherein the data processing system thatoperates upon the energy usage data uses a polynomial decomposition ofwater flow according to changes in power of the water pump in obtainingthe water usage estimate of a pump working away from its averageoperating condition; and preparing the water record based upon theestimated water usage using the data processing computer system.
 3. Amethod of providing an automated water record for an irrigation systemusing ground water extraction and that includes an electrical water pumpthat operates using electrical energy and which is connected to anenergy meter that periodically provides for a previous period of timeenergy usage data for the previous period of time, the method comprisingthe steps of: receiving and storing the energy usage data associatedwith the irrigation system for the previous period of time in adatabase; using the energy usage data for the previous period of time toestimate water usage for the previous period of time using a dataprocessing computer system coupled to the database that operates uponthe energy usage data, wherein the data processing system that operatesupon the energy usage data uses machine learning in obtaining the waterusage estimate based on a type of pump curve that relates water flow,horse power, and feet of head; and preparing the water record based uponthe estimated water usage using the data processing computer system. 4.The method according to claim 1, wherein the previous period of time isat least a 24 hour period.
 5. The method according to claim 1, whereinthe previous period of time is at least a one hour period.
 6. The methodaccording to claim 1, wherein the previous period of time is a oneminute period.
 7. The method according to claim 2, wherein the previousperiod of time is at least a 24 hour period.
 8. The method according toclaim 2, wherein the previous period of time is at least a one hourperiod.
 9. The method according to claim 2, wherein the previous periodof time is a one minute period.
 10. The method according to claim 3,wherein the previous period of time is at least a 24 hour period. 11.The method according to claim 3, wherein the previous period of time isat least a one hour period.
 12. The method according to claim 3, whereinthe previous period of time is a one minute period.